CJASN
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Published ahead of print on November 2, 2006
Clin J Am Soc Nephrol 2: 112-120, 2007
© 2007 American Society of Nephrology
doi: 10.2215/CJN.00910306
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
CJN.00910306v1
2/1/112    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Torres, V. E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Torres, V. E.

Hereditary Disease

Magnetic Resonance Measurements of Renal Blood Flow and Disease Progression in Autosomal Dominant Polycystic Kidney Disease

Vicente E. Torres*, Bernard F. King*, Arlene B. Chapman{dagger}, Marijn E. Brummer{dagger}, Kyongtae T. Bae**, James F. Glockner*, Kraisthith Arya{dagger}, Dana Risk{dagger}, Joel P. Felmlee*, Jared J. Grantham{ddagger}, Lisa M. Guay-Woodford§, William M. Bennett||, Saulo Klahr**, Catherine M. Meyers, Xiaoling Zhang**, Paul A. Thompson**, J. Philip Miller**; and the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP)

* Mayo Clinic College of Medicine, Rochester, Minnesota; {dagger} Emory University School of Medicine, Atlanta, Georgia; {ddagger} University of Kansas Medical Center, Kansas City, Kansas; § University of Alabama, Birmingham Alabama; || Northwest Renal Clinic, Portland, Oregon; National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases/Division of Kidney, Urologic, and Hematologic Diseases, Bethesda, Maryland; and ** Washington University, St. Louis, Missouri

Address correspondence to: Dr. Vicente E. Torres, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55901. Phone: 507-284-7527; Fax: 507-266-9315; E-mail: torres.vicente{at}mayo.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
Whether changes in renal blood flow (RBF) are associated with and possibly contribute to cystic disease progression in autosomal dominant polycystic kidney disease (ADPKD) has not been ascertained. The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) was created to develop imaging techniques and analyses to evaluate progression. A total of 131 participants with early ADPKD had measurements of RBF and total kidney (TKV) and cyst (TCV) volumes by magnetic resonance and of GFR by iothalamate clearance at baseline and 1, 2, and 3 yr. The effects of age, gender, body mass index, hypertension status, mean arterial pressure (MAP), TKV, TCV, RBF, renal vascular resistance (RVR), GFR, serum uric acid, HDL and LDL cholesterol, 24-h urine volume, sodium (UNaE) and albumin (UAE) excretions, and estimated protein intake were examined at baseline on TKV, TCV, and GFR slopes. TKV and TCV increased, RBF decreased, and GFR remained stable. TKV, TCV, RVR, serum uric acid, UAE, UNaE, age, body mass index, MAP, and estimated protein intake were positively and RBF and GFR negatively correlated with TKV and TCV slopes. TKV, RBF, UNaE, and UAE were independent predictors of TKV and TCV slopes (structural disease progression). TKV, TCV, RVR, and MAP were negatively and RBF positively correlated with GFR slopes. Regression to the mean confounded the analysis of GFR slopes. TKV and RBF were independent predictors of GFR decline (functional disease progression). In ADPKD, RBF reduction (1) parallels TKV increase, (2) precedes GFR decline, and (3) predicts structural and functional disease progression.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 Disclosures
 References
 
Autosomal dominant polycystic kidney disease (ADPKD) is a systemic, inherited disorder that is characterized by development of renal cysts and enlargement and, at a late stage, decline in renal function (1). Hypertension, vasoconstriction, and remodeling of small renal arteries and arterioles occur early, before development of renal insufficiency (2,3). It is possible that these are secondary to the development of renal cysts (4). The expression of polycystin 1 and polycystin 2 in vascular smooth muscle (5–7) and endothelial cells (8) and the functional alterations of vascular smooth muscle (9,10) and endothelial cells (11,12) with PKD1 or PKD2 mutations raise the possibility of a direct role of these mutations in their pathogenesis. The extent to which changes in renal blood flow (RBF) and vascular remodeling are associated with and possibly contribute to cystic disease progression and functional decline has not been ascertained, in part because of the lack of noninvasive methods. The National Institutes of Health–sponsored Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) was created to develop imaging techniques and analyses to follow disease progression and evaluate treatments for ADPKD (13–16). Here, we examine the changes in RBF, renal volume, and GFR over time and whether changes in RBF are associated with and possibly contribute to the structural and functional progression of ADPKD.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 Disclosures
 References
 
Study Organization
CRISP clinical centers included the Mayo Clinic College of Medicine and University of Alabama at Birmingham, Emory University, and University of Kansas Medical Center. Washington University served as the data coordinating and image analysis center (13–16). The data coordinating and image analysis center collected images that were transferred from the collaborating institutions in Digital Imaging and Communications in Medicine format, segmented and analyzed the images, stored and recorded the results, and performed statistical analysis. Magnetic resonance imaging (MRI) determinations of RBF were performed at the Mayo Clinic College of Medicine on a Signa CV/I 1.5T scanner (GE Medical Systems, Milwaukee, WI) and Emory University on a Gyroscan Intera 1.5T scanner (Philips Medical Systems, Best, The Netherlands).

Study Protocol
Patients with ADPKD were eligible when they were older than 15 and younger than 46 yr and had a measured or estimated creatinine clearance of >70 ml/min (13). Patients were ineligible when they had undergone renal surgery, had cyst drainage procedures, were unable to undergo breath-hold MRI, or had other medical conditions that potentially affected renal function. Hypertension status was defined as a previous diagnosis and current use of antihypertensive medications or systolic and diastolic BP >140/90 mmHg on three consecutive visits.

After signing an informed written consent, enrolled patients were scheduled for 2-d evaluations in the General Clinical Research Center at baseline and 1 (YR1), 2 (YR2), and 3 (YR3) yr later. Each time, they were instructed to continue their medications, to discontinue any nonsteroidal anti-inflammatory medications for at least 7 d before evaluation, and not to initiate diuretic therapy within 14 d of evaluation. During the day before admission, patients collected a 24-h urine sample for determination of creatinine, urea nitrogen, sodium (UNaE), and albumin (UAE) excretions. Weight, height, and body mass index (BMI) were measured at admission. BP were measured in the morning, before antihypertensive medication intake, in the left and right arms after being seated for at least 5 min on three occasions 3 min apart using an oscillometric measuring device. Blood was obtained before GFR studies for the determination of blood hemoglobin, serum electrolytes, liver enzymes, and lipid profiles using standard laboratory techniques. GFR was measured by a nonradiolabeled iothalamate clearance technique with sonographic monitoring of bladder emptying (15). The mean coefficient of variation was 4.9%. GFR also was estimated using the Modification of Diet in Renal Disease (MDRD) equation: 186.3 x serum creatinine–1.154 x age–0.203 x 0.742 (if female) x 1.212 (if black) (17). Protein intake (g/d) was estimated using the equation 6.25 x [urine urea nitrogen in g/d + (0.03 x weight in kg)] (18).

Measurements of Total Kidney Volume and Total Cyst Volume
Magnetic resonance imaging studies were performed in the morning before medication intake and breakfast (13–16). Coronal T2-weighted images (single- shot fast spin-echo/half-Fourier acquired single-shot turbo spin-echo) and gadolinium-enhanced three-dimensional volume-interpolated spoiled-gradient echo coronal T1-weighted images were obtained (3-mm slice thickness). The volumes of individual kidneys were measured in T1-weighted images with a stereology method and calculated from the set of contiguous images by summing the products of the area measurements and slice thickness. A region-based threshold method was used to calculate cyst volumes (19). Reliability coefficients were 0.998 for total kidney volume (TKV) and 0.961 for total cyst volume (TCV) in repeatedly acquired images on individual patients. The average coefficients of variation of the TKV and TCV measurements in the repeated analysis of 99 images were 0.01 and 0.26%, respectively.

Measurements of RBF
Thick-section, oblique-axial, two-dimensional, phase-contrast, breath-hold MR angiograms were obtained along the course of each renal artery and acted as reference images (14). The MR flow measurements were obtained perpendicular to the oblique-axial, two-dimensional reference images of the renal arteries using a cardiac-gated, two-dimensional, fast gradient-echo, phase-contrast pulse sequence. MR blood flow pulse sequence parameters were reported previously (14). The velocity encoding value was 100 cm/s, and flow acquisition was obtained in the slice direction. Flow analysis was performed using FLOW software Version 4 (Medis, Leiden, The Netherlands). Semiautomated or manual techniques were used for definition of the vessel borders in the flow images depending on image quality. Vessel area estimations were made in images at all phases of the cardiac cycle. RBF measurements at Emory University in YR3 of the study were not comparable to those in the previous years because of software upgrades. Therefore, these values were not used in the analysis. Renal vascular resistance (RVR) was calculated as mean arterial pressure (MAP)/RBF x 80,000 (20). Renal plasma flow and filtration fraction (FF) were calculated using accepted formulas. The average interreviewer coefficient of variation of RBF measurements using the same image acquisitions is 2.5% with a reliability coefficient of 0.983 (14). The average coefficient of variation for repeated RBF measurements using independent acquisitions is 2.9 or 6.0%, depending on whether the acquisitions included the same or a different scouting process (21).

Statistical Analyses
The data were examined using SAS system software (SAS Institute, Cary, NC). Mixed model ANOVA (PROC MIXED; SAS) was used to detect the statistical significance of changes over time. Correlations between baseline values and slopes estimated by individual regressions within each participant were computed as Pearson r values. For ensuring that outlier cases did not distort the correlations, Spearman values also were checked (these did not differ greatly and are not presented).


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 Disclosures
 References
 
Baseline Characteristics and Longitudinal Changes
A total of 131 patients participated in this study (58 at the Mayo Clinic and 73 at Emory University). Forty-nine were male, and 82 were female. The mean age at entry into the study was 31.5 yr (range 15 to 46). Table 1 summarizes the relevant clinical, radiologic, and laboratory parameters at baseline, YR1, YR2, and YR3. BMI tended to increase and reached statistical significance at YR3. MAP decreased significantly from baseline to YR1 and remained stable during YR2 and YR3. Baseline TKV and TCV were 891 and 316 ml, respectively. TKV and TCV increased significantly and RBF decreased significantly from year to year. Despite the lower RBF, RVR at YR1 remained stable as a result of the reduction in MAP. Significant increases in RVR were detected at YR2 and YR3. GFR (measured by the iothalamate clearance or estimated by the MDRD equation) was stable at baseline, YR1, and YR2. Compared with baseline, mean measured and estimated GFR (eGFR) values were 2.8 and 5.3 ml/min per 1.73 m2 lower in YR3, but only the change in eGFR reached statistical significance. The decreasing RBF and stable GFR resulted in an increase in FF that was statistically significant in YR2 and YR3. The increase in FF in YR3 was accompanied by a significant increase in UAE. Serum HDL cholesterol levels were significantly lower in YR1, YR2, and YR3 compared with baseline values. No significant changes were detected in serum LDL cholesterol or uric acid. Twenty-four-hour urine output was significantly higher in YR1, YR2, and YR3, whereas UNaE remained unchanged. Estimated protein intake was significantly higher in YR2 and YR3 compared with baseline.


View this table:
[in this window]
[in a new window]

  		Table 1. Clinical, radiologic, and laboratory parametersa

 
Correlations between Baseline Parameters and Indices of Disease Progression
Table 2 shows the correlations between relevant baseline clinical, radiologic, and laboratory parameters and the slopes in TKV (or TCV) and in measured GFR or eGFR. TKV, TCV, RVR, serum uric acid, UAE, and UNaE were positively correlated with TKV slopes during the 3 yr of follow-up (Figure 1). Weak positive correlations with age, BMI, MAP, and estimated protein intake also were detected. RBF, eGFR, and measured GFR were negatively correlated with TKV slopes (Figure 1). A weak negative correlation with HDL cholesterol also was detected. Similar correlations were observed with TCV slopes. TKV, TCV, RVR, and MAP were negatively and RBF positively correlated with measured GFR slopes (Figure 2) but not with eGFR slopes. Measured GFR at baseline was negatively correlated with the measured GFR slope (Figure 2) but not with the eGFR slope. Similarly, the eGFR at baseline was negatively correlated with the eGFR slope but not with the measured GFR slope. This suggests that these correlations are due to regression to the mean, rather than disease progression (see Discussion).


View this table:
[in this window]
[in a new window]

  		Table 2. Pearson correlation coefficients among baseline clinical, radiologic, or laboratory parameters and the slopes of log 10 TKV, log 10 TCV, measured GFR or estimated MDRD GFR

 

Figure 1
View larger version (33K):
[in this window]
[in a new window]
[as a PowerPoint slide]
  		Figure 1. Relationship among total kidney volume slopes and baseline log 10 kidney volumes, renal blood flows, iothalamate clearances, log 10 urine albumin excretions, serum uric acid levels, and urine sodium excretions.

 

Figure 2
View larger version (33K):
[in this window]
[in a new window]
[as a PowerPoint slide]
  		Figure 2. Relationship among iothalamate clearance slopes and baseline log 10 kidney volumes, iothalamate clearances, renal blood flows, and renal vascular resistances.

 
Regression Analysis for Structural Disease Progression
Multiple regression models were used to determine which baseline variables were independently predictive of structural progression reflected by TKV (Table 3) or TCV slopes (data not shown). For the model of TKV slope with baseline TKV in the model (Table 3, columns 1 through 3), three baseline variables contributed independently (TKV and UNaE positive, RBF negative). For the model of TKV slope with baseline TKV excluded from the model (Table 3, columns 4 through 6), three variables contributed independently (UAE and UNaE positive, RBF negative). Similar results were obtained when TCV slope was used to examine structural progression, with the same variables showing similar results in these models (data not shown). Replacing RVR for RBF showed that RVR also was a strong independent positive predictor of TKV (found significant with baseline TKV excluded or included in the model) and TCV (found significant with baseline TCV excluded or included in the model) slopes (data not shown).


View this table:
[in this window]
[in a new window]

  		Table 3. Regression model predicting structural disease progression (TKV) from baseline parameters

 
Regression Analysis for Functional Disease Progression
Multiple regression models were used to determine which baseline variables are independently predictive of functional progression (estimated by GFR slope), including either TKV (Table 4) or TCV (data not shown) as predictors. For the model of GFR slope with baseline GFR in the model (Table 3, columns 1 through 3), four baseline variables contributed independently (RBF and UAE positive, TKV and GFR negative). The negative relationship between baseline GFR and GFR slopes is consistent with regression to the mean (see the Discussion section). The baseline RBF result (significant in the model with baseline GFR, NS when baseline GFR is excluded) is interesting. The baseline RBF-GFR slope correlation is significant but not strong (r = 0.233, P = 0.010). When the effect of baseline GFR is statistically partialed out (as is done in a multiple regression), the baseline RBF-GFR slope correlation is much stronger and more significant (r = 0.512, P < 0.001). For the model of GFR slope with baseline GFR excluded from the model (Table 4, columns 4 through 6), three baseline variables contributed independently (age and UAE positive, TKV negative). Similar results were obtained when TKV was replaced by TCV or when RBF was replaced by RVR in the model (data not shown).


View this table:
[in this window]
[in a new window]

  		Table 4. Regression models predicting functional disease progression (GFR) from baseline parameters

 
Determinants of RBF Decline
To identify factors that are responsible for the decline in RBF, we performed a regression analysis using potentially relevant independent baseline variables or variables averaged during the whole period of observation (Table 5). These models accounted for only a small fraction of the variability in the rate of decline in RBF (R2 shown in Table 5). The presence of hypertension and lower levels of serum HDL cholesterol were independently associated with a faster decline of RBF. Conversely, the serum uric acid concentration was a negative predictor of RBF decline.


View this table:
[in this window]
[in a new window]

  		Table 5. Regression model predicting RBF slopes from parameters at baseline or averaged during the whole period of observation (baseline through year 3)

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
Disclosures
 References
 
In ADPKD, renal growth is characterized by slowly enlarging cystic disease throughout the cortex and the medulla (22). These cysts are clonal outpouchings from single cells along a limited number of collecting ducts and nephrons. The events that initiate the clonal transformation include loss of heterozygosity or a somatic mutation of the allele that is unaffected by the germline mutation or possibly somatic mutations of other related genes. When the cysts reach approximately 2 mm in diameter, the majority become disconnected from the tubules, whereupon their growth requires chloride-driven fluid secretion into the lumen. Compression of the renal arteries and parenchyma by expanding cysts may lead to renal ischemia, activation of the renin-angiotensin system, interstitial inflammation and fibrosis, apoptosis of tubular epithelial cells, vascular sclerosis, and eventually renal failure. Alternatively, mutations in the PKD genes may cause directly cyst development, apoptosis of tubular epithelial cells, and remodeling of the renal vasculature, together leading to renal failure. Irrespective of the underlying mechanism, renal hemodynamic changes occur early in ADPKD and may contribute to the development of hypertension and renal insufficiency.

Although all patients with PKD1 or PKD2 mutations develop cysts, progression to renal failure varies widely between and within families (23). The disease is more severe in patients with PKD1 mutations. Among those with PKD2 mutations, it is more severe in male individuals (24). Modifier genes and environmental factors contribute to this variability. Factors that predict the risk for renal failure have been identified (25). In addition to kidney size, presence of hypertension or proteinuria, hyperlipidemia, low HDL cholesterol levels, and smoking are associated with an increased risk. Whether they increase the risk for cyst growth or simply promote nonspecific vascular, glomerular, or tubulointerstitial mechanisms is not known.

The lack of appropriate noninvasive methods has been an impediment to the study of renal hemodynamics and microvasculature in ADPKD. The few studies in this area have used measurements of RBF that rely on determinations of renal clearance of para-aminohippurate (26–28). These measurements assume normal renal extraction of para-aminohippurate, which is not likely in ADPKD, particularly in the presence of renal insufficiency. CRISP was created to develop imaging techniques and analyses to follow disease progression inADPKD (13–16). Goals of CRISP included determining whether MR can reliably measure RBF, whether changes can be detected over a short period of time and be used as a surrogate marker of progression, and whether reductions in RBF predict structural and/or functional progression. We have published validation studies demonstrating the absolute requirement for precise MR blood flow pulse sequence parameters and the accuracy of the measurements in phantoms and their reproducibility in volunteers with ADPKD (14).

The results of our study provide insight to the chronology of RBF changes in relation to changes in kidney volume and GFR. Consistent with previous cross-sectional studies, reductions in RBF occur in parallel with the increases in TKV and precede the decline in GFR. GFR is maintained at least in part by an increase in FF, which is accompanied by an increase in UAE. Reductions in RBF may be secondary to changes in kidney volume, neurohumoral or local mediators, and intrinsic vascular factors. The early reduction in RBF that independently predicts TKV and TCV increases supports previous studies suggesting that renal ischemia or related factors, such as activation of the renin-angiotensin system, promote renal cystogenesis (29–31). Among the remaining baseline parameters that are correlated significantly with TKV or TCV slopes in the univariate analysis (age, BMI, serum uric acid, UAE, and UNaE), only UAE and UNaE were significant independent predictors. Whether increased UAE contributes to cystogenesis is not known, but albumin has mitogenic and apoptotic effects on proximal (OK and LLCPK cells) and collecting/distal (MDCK cells) tubular epithelial cells (32–35). The association of high UNaE with a greater increase in TKV and TCV may be consistent with a cystogenic effect of a high sodium diet, as observed in an animal model of polycystic kidney disease (29). Of note, the daily sodium intakes in motivated study participants (reflected by mean UNaE between 181 and 190 mEq during baseline through YR3) were higher than the recommended 90 mEq, despite good BP control. These observations suggest that control of dietary sodium may be important for the control not only of BP but also of cystic disease progression.

The relatively small changes in GFR that were observed during the study limited the ability to detect significant baseline predictors of GFR decline. Nevertheless, baseline TKV and TCV, RVR, MAP, and LDL cholesterol were negatively and baseline RBF positively correlated with GFR slopes in the univariate analysis. Only TKV and TCV were strong independent predictors of renal functional decline in the regression analysis when baseline GFR was not included in the model. Low baseline RBF also were predictive of renal functional decline when baseline GFR was used in the model. The failure to detect an effect of baseline RBF on GFR slopes in the model without baseline GFR may be explained by the strong inverse correlation between baseline GFR and the slope of GFR, either measured by iothalamate clearance or estimated by the MDRD equation. This inverse correlation is likely explained by regression to the mean. This is particularly relevant in a population with preserved renal function, because of the wider physiologic variability of GFR and lower precision of GFR measurements within the normal range. Other factors, such as hyperfiltration, that are known to occur in early ADPKD (36) and possibly pharmacologic interventions are less likely to play a role. Because regression to the mean may be more obvious in the participants with milder ADPKD and lower UAE, it also may explain the unexpected positive predictive value of UAE on GFR slopes that was noted in the regression analysis. Overall, the analysis of the effects of baseline parameters on renal functional decline point to the importance of renal cystic disease and vascular changes as determinants of functional progression in ADPKD.

In a previous cross-sectional analysis of baseline parameters in the CRISP study, RBF (or RVR) but not TKV (or TCV) was found to be a strong independent predictor of GFR. The failure to identify TKV as an independent predictor of baseline GFR may in part be explained by the inclusion criteria requiring a measured or estimated creatinine clearance of >70 ml/min. The strong effect of RBF likely is explained by the physiologic relationship between RBF and GFR. In this longitudinal study, baseline TKV is a stronger predictor of GFR decline than baseline RBF. Nevertheless, both studies identify renal hemodynamic parameters (RBF and RVR) as significant predictors of GFR or GFR decline independent from TKV and TCV. The study cannot determine whether the changes in RBF are secondary to increases in intrarenal pressure related to cyst growth or to other alterations that directly cause arterial constriction and remodeling.

Whereas TKV and RBF are significant predictors of structural progression (increase in TKV) and functional progression (GFR decline), the factors that predict RBF decline are less clear. The results of the study suggest that the presence of hypertension or factors related to hypertension and reduced levels of HDL cholesterol likely play a role. In essential hypertension, a decreased renal vasodilatory response to l-arginine has been associated with decreased serum levels of HDL cholesterol (37). The study unexpectedly detected a positive, rather than a negative, effect of serum uric acid, a factor that has been proposed to play a role in the development of renal microvascular disease (38), on the RBF slopes. This result is difficult to interpret because a number of patients in the study were treated with allopurinol. Importantly, the parameters that were measured in this study accounted for a very small fraction of the variability of the decline in RBF, pointing to the limitations of the current understanding of renal hemodynamics and vascular remodeling in ADPKD.


   Disclosures
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
References
 
None.


   Acknowledgments
 
This study was supported by the CRISP Study UO1 cooperative agreements (DK56956, DK56943, DK56957, and DK56961), the Mayo Clinic General Clinical Research grant MO1-RR00585, and the Emory University General Clinical Research grant MO1-RR00039 of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health.

We are indebted to the study coordinators, who worked with professional grace and high competence to bring this project to conclusion: Lorna Stevens, Kristin Cornwell, Diane Watkins, and Sharon Langley. We thank Mary Virginia Gaines for managerial assistance.


   Footnotes
 
Published online ahead of print. Publication date available at www.cjasn.org.

Received March 15, 2006. Accepted September 15, 2006.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Disclosures
 References
 

  1. Grantham JJ, Chapman AB, Torres VE: Volume progression in autosomal dominant polycystic kidney disease: The major factor determining clinical outcomes. Clin J Am Soc Nephrol1 :148 –157,2006
  2. Schacht F: Hypertension and Vascular Studies in Congenital Polycystic Kidney [master’s thesis], St. Paul, University of Minnesota, 1930
  3. Zeier M, Fehrenbach P, Geberth S, Mohring K, Waldherr R, Ritz E: Renal histology in polycystic kidney disease with incipient and advanced renal failure. Kidney Int42 :1259 –1265,1992[Medline]
  4. Gabow PA, Chapman AB, Johnson AM, Tangel DJ, Duley IT, Kaehny WD, Manco-Johnson M, Schrier RW: Renal structure and hypertension in autosomal dominant polycystic kidney disease. Kidney Int38 :1177 –1180,1990[Medline]
  5. Griffin MD, Torres VE, Grande JP, Kumar R: Vascular expression of polycystin. J Am Soc Nephrol8 :616 –626,1997[Abstract]
  6. Torres VE, Cai Y, Chen X, Wu GQ, Geng L, Cleghorn KA, Johnson CM, Somlo S: Vascular expression of polycystin 2. J Am Soc Nephrol12 :1 –9,2001[Abstract/Free Full Text]
  7. Qian Q, Li M, Cai Y, Ward CJ, Somlo S, Harris PC, Torres VE: Analysis of the polycystins in aortic vascular smooth muscle cells. J Am Soc Nephrol14 :2280 –2287,2003[Abstract/Free Full Text]
  8. Ibraghimov-Beskrovnaya O, Dackowski WR, Foggensteiner L, Coleman N, Thiru S, Petry LR, Burn TC, Connors TD, Van Raay T, Bradley J, Qian F, Onuchic LF, Watnick TJ, Piontek K, Hakim RM, Landes GM, Germino GG, Sandford R, Klinger KW: Polycystin: In vitro synthesis, in vivo tissue expression, and subcellular localization identifies a large membrane-associated protein Proc Natl Acad Sci U S A94 :6397 –6402,1997[Abstract/Free Full Text]
  9. Qian Q, Hunter LW, Li M, Marin-Padilla M, Prakash YS, Harris PC, Somlo S, Torres VE, Sieck GC: Pkd2 haploinsufficiency alters intracellular calcium in vascular smooth muscle cells. Hum Mol Genet12 :1875 –1880,2003[Abstract/Free Full Text]
  10. Kip SN, Hunter LW, Ren Q, Harris PC, Somlo S, Torres VE, Sieck GC, Qian Q: [Ca2+]i reduction increases cellular proliferation and apoptosis in vascular smooth muscle cells: Relevance to the ADPKD phenotype. Circ Res96 :873 –880,2005[Abstract/Free Full Text]
  11. Wang D, Iversen J, Wilcox CS, Strandgaard S: Endothelial dysfunction and reduced nitric oxide in resistance arteries in autosomal-dominant polycystic kidney disease. Kidney Int64 :1381 –1388,2003[CrossRef][Medline]
  12. Kocaman O, Oflaz H, Yekeler E, Dursun M, Erdogan D, Demirel S, Alisir S, Turgut F, Mercanoglu F, Ecder T: Endothelial dysfunction and increased carotid intima-media thickness in patients with autosomal dominant polycystic kidney disease. Am J Kidney Dis43 :854 –860,2004[CrossRef][Medline]
  13. Chapman A, Guay-Woodford L, Grantham JJ, Torres V, Bae K, Baumgarten D, Kenney P, King B, Glockner J, Wetzel L, Brummer M, O’Neill C, Robbin M, Bennett W, Klahr S, Hirschman G, Kimmel P, Thompson P, Miller J: Renal structure in early autosomal dominant polycystic kidney disease (ADPKD); the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) Cohort. Kidney Int64 :1035 –1045,2003[CrossRef][Medline]
  14. King BF, Torres VE, Brummer ME, Chapman AB, Bae KT, Glockner JF, Arya K, Felmlee JP, Grantham JJ, Guay-Woodford LM, Bennett WM, Klahr S, Hirschman GH, Kimmel PL, Thompson PA, Miller JP: Magnetic resonance measurements of renal blood flow as a marker of disease severity in autosomal-dominant polycystic kidney disease. Kidney Int64 :2214 –2221,2003[CrossRef][Medline]
  15. Rule AD, Torres VE, Chapman AB, J GJ, Guay-Woodford LM, Bae KT, Klahr S, Bennett WM, Meyers CM, Thompson PA, Miller JP; CRISP: Comparison of methods for determining renal function decline in early autosomal dominant polycystic kidney disease: The Consortium of Radiologic Imaging studies of Polycystic Kidney Disease Cohort. J Am Soc Nephrol17 :854 –862,2006[Abstract/Free Full Text]
  16. Grantham JJ, Torres VE, Chapman AB, Guay-Woodford LM, Bae KT, King BF Jr, Wetzel LH, Baumgarten DA, Kenney PJ, Harris PC, Klahr S, Bennett WM, Hirschman GN, Meyers CM, Zhang X, Zhu F, Miller JP: Volume progression in polycystic kidney disease. N Engl J Med354 :2122 –2130,2006[Abstract/Free Full Text]
  17. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med130 :461 –470,1999[Abstract/Free Full Text]
  18. Maroni BJ, Steinman TI, Mitch WE: A method for estimating nitrogen intake of patients with chronic renal failure. Kidney Int27 :58 –65,1985[Medline]
  19. Bae KT, Commean PK, Lee J: Volumetric measurement of renal cysts and parenchyma using MRI: Phantoms and patients with polycystic kidney disease. J Comput Assist Tomogr24 :614 –619,2000[CrossRef][Medline]
  20. De Leeuw P, Kho T, Falke H, Birkenhager W, Wester A: Haemodynamic and endocrinological profile of essential hypertension. Acta Med Scand Suppl622 :1 –86,1978
  21. Brummer ME, Dambreville S, King BF, Chapman AB, Glockner J, Torres VE, Wallin A, Bae KT, Miller JP, Ladson J, Agress L, Frakes D, Yoganathan A: Renal Blood Flow Measurement by Breathhold Phase-Velocity MRI: A Validation Study. Presented at the annual meeting of the ISMRM, Miami, FL, May 7–13,2005
  22. Grantham JJ: Lillian Jean Kaplan International Prize for advancement in the understanding of polycystic kidney disease. Understanding polycystic kidney disease: A systems biology approach. Kidney Int64 :1157 –1162,2003[CrossRef][Medline]
  23. Rossetti S, Burton S, Strmecki L, Pond GR, San Millan JL, Zerres K, Barratt TM, Ozen S, Torres VE, Bergstralh EJ, Winearls CG, Harris PC: The position of the polycystic kidney disease 1 (PKD1) gene mutation correlates with the severity of renal disease. J Am Soc Nephrol13 :1230 –1237,2002[Abstract/Free Full Text]
  24. Hateboer N, van Dijk MA, Bogdanova N, Coto E, Saggar-Malik AK, San Millan JL, Torra R, Breuning M, Ravine D: Comparison of phenotypes of polycystic kidney disease types 1 and 2. Lancet353 :103 –107,1999[CrossRef][Medline]
  25. Johnson A, Gabow P: Identification of patients with autosomal dominant polycystic kidney disease at highest risk for end-stage renal disease. J Am Soc Nephrol8 :1560 –1567,1997[Abstract]
  26. Chapman AB, Johnson A, Gabow PA, Schrier RW: The renin-angiotensin-aldosterone system and autosomal dominant polycystic kidney disease. N Engl J Med323 :1091 –1096,1990[Abstract]
  27. Torres VE, Wilson DM, Burnett JCJ, Johnson CM, Offord KP: Effect of inhibition of converting enzyme on renal hemodynamics and sodium management in polycystic kidney disease. Mayo Clin Proc66 :1010 –1017,1991[Medline]
  28. Watson M, Macnicol A, Allan P, Wright A: Effects of angiotensin-converting enzyme inhibition in adult polycystic kidney disease. Kidney Int41 :206 –210,1992[Medline]
  29. Keith DS, Torres VE, Johnson CM, Holley KE: Effect of sodium chloride, enalapril, and losartan on the development of polycystic kidney disease in Han:SPRD rats. Am J Kidney Dis24 :491 –498,1994[Medline]
  30. Ogborn M, Sareen S, Pinette G: Cilazapril delays progression of hypertension and uremia in rat polycystic kidney disease. Am J Kidney Dis26 :942 –946,1995[Medline]
  31. Kennefick T, Al-Nimri M, Oyama T, Thompson M, Kelly F, Chapman J, Anderson S: Hypertension and renal injury in experimental polycystic kidney disease. Kidney Int56 :2181 –2190,1999[CrossRef][Medline]
  32. Dixon R, Brunskill NJ: Albumin stimulates p44/p42 extracellular-signal-regulated mitogen-activated protein kinase in opossum kidney proximal tubular cells. Clin Sci (Lond)98 :295 –301,2000[Medline]
  33. Erkan E, De Leon M, Devarajan P: Albumin overload induces apoptosis in LLC-PK(1) cells. Am J Physiol Renal Physiol280 :F1107 –F1114,2001[Abstract/Free Full Text]
  34. Lee EM, Pollock CA, Drumm K, Barden JA, Poronnik P: Effects of pathophysiological concentrations of albumin on NHE3 activity and cell proliferation in primary cultures of human proximal tubule cells. Am J Physiol Renal Physiol285 :F748 –F757,2003[Abstract/Free Full Text]
  35. Erkan E, Devarajan P, Schwartz GJ: Apoptotic response to albumin overload: Proximal vs. distal/collecting tubule cells. Am J Nephrol25 :121 –131,2005[CrossRef][Medline]
  36. Wong H, Vivian L, Weiler G, Filler G: Patients with autosomal dominant polycystic kidney disease hyperfiltrate early in their disease. Am J Kidney Dis43 :624 –628,2004[CrossRef][Medline]
  37. Bello E, Caramelo C, Martell N, Alcazar JM, Gonzalez J, Lopez MD, Ruilope LM, Gonzalez FR, Rovira AM, Gazapo R, Soldevilla MJ, Casado S: Impairment of renal vasodilation with l-arginine is related to more severe disease in untreated hypertensive patients. Hypertension38 :907 –912,2001[Abstract/Free Full Text]
  38. Nakagawa T, Kang DH, Feig D, Sanchez-Lozada LG, Srinivas TR, Sautin Y, Ejaz AA, Segal M, Johnson RJ: Unearthing uric acid: An ancient factor with recently found significance in renal and cardiovascular disease. Kidney Int69 :1722 –1725,2006[CrossRef][Medline]



This article has been cited by other articles:


Home page
 Exp. Biol. Med.Home page
V. H. Gattone II, T. M. Siqueira Jr, C. R. Powell, C. M. Trambaugh, J. E. Lingeman, and A. L. Shalhav
Contribution of Renal Innervation to Hypertension in Rat Autosomal Dominant Polycystic Kidney Disease
Experimental Biology and Medicine, August 1, 2008; 233(8): 952 - 957.
[Abstract] [Full Text] [PDF]

Home page
 CJASNHome page
A. B. Chapman
Approaches to Testing New Treatments in Autosomal Dominant Polycystic Kidney Disease: Insights from the CRISP and HALT-PKD Studies
Clin. J. Am. Soc. Nephrol., July 1, 2008; 3(4): 1197 - 1204.
[Abstract] [Full Text] [PDF]

Home page
 Nephrol Dial TransplantHome page
T. I. Steinman
Renal and cardiac effects of antihypertensive treatment with ramipril versus metoprolol in autosomal dominant polycystic kidney disease
Nephrol. Dial. Transplant., February 1, 2008; 23(2): 431 - 433.
[Full Text] [PDF]

Home page
 Am. J. Physiol. Renal Physiol.Home page
I. Zafar, Y. Tao, S. Falk, K. McFann, R. W. Schrier, and C. L. Edelstein
Effect of statin and angiotensin-converting enzyme inhibition on structural and hemodynamic alterations in autosomal dominant polycystic kidney disease model
Am J Physiol Renal Physiol, September 1, 2007; 293(3): F854 - F859.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
CJN.00910306v1
2/1/112    most recent
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Torres, V. E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Torres, V. E.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS